{"type":"ScholarlyArticle","title":"Learning Structural Decision Trees from Examples","authors":[{"name":"Larry Watanabe","slug":"larry-watanabe","given":"Larry","family":"Watanabe","affiliation":""},{"name":"Larry A. Rendell","slug":"larry-a-rendell","given":"Larry A.","family":"Rendell","affiliation":""}],"abstract":"STRUCT is a system that learns structural decision trees from positive and negative examples. The algorithm uses a modification of Pagallo and Haussler's FRINGE algorithm to construct new features in a first-order representation. Experiments compare the effects of different hypothesis evaluation strategies, domain representation, and feature construction. STRUCT is also compared with Quinlan's FOIL on two domains. The results show that a modified FRINGE algorithm improves accuracy, but that it is sensitive to the distribution of the examples.","doi":null,"url":"https://ijcai.org/proceedings/1991/113","pdf":"https://ijcai.org/proceedings/1991/0113.pdf","markdown":"https://ijcai.org/proceedings/1991/113.md","bibtex":"https://ijcai.org/proceedings/1991/bibtex/113","volume":{"slug":"1991","title":"Proceedings of the Twelfth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 1991","url":"https://ijcai.org/proceedings/1991/","year":1991,"location":"Sydney, New South Wales, Australia","dates":"24-30 August 1991","editors":"John Mylopoulos and Ray Reiter","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Learning Be Knowledge Acquisition","track":"Learning Be Knowledge Acquisition","number":113,"pages":{"first":770,"last":775},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}